1. Abstract
Background
Methods
Results: Demographic characteristics
Aims: 1) To assess technology and mHealth use patterns and
preferences among inpatient detoxification program participants.
2) To assess privacy concerns related to mHealth communication
in addiction treatment among this population.
Methodology: Research staff approached subjects between
December 2014 - July 2015 and administered a 72-item survey.
Results: Use patterns and preferences were consistent with
nationwide technology and mHealth adoption trends, with added
sensitivity to, and concern for, privacy of text message content.
Research staff approached inpatient detoxification program
patients at Bellevue Hospital Center between December 2014 -
July 2015 and administered a 72-item survey.
The survey was comprised of closed-ended (Likert scale, binary
yes/no, multiple choice) and open-end items.
Interviews took approximately 30 minutes and participants were
provided with $5 transit vouchers.
36 items in 4 domains assessed privacy concerns to mHealth use
in addiction treatment: demographic/clinical characteristics;
technology use patters; mHealth adoption preferences; comfort
with SUD-related terminology.
Acceptability and privacy concerns for mHealth interventions among inpatient detoxification program patients
Grazioli F, Perna M, Thomas A, MD, Lee JD MD, MSc, Tofighi B MD, MSc
SARET Program, New York University
 Mobile and wireless devices to improve health outcomes
(‘mHealth’) and healthcare services and research are effective
in treating behavioral health and substance use disorders.
 53% of US adult mobile phone owners own smartphones.
 31% of mobile phone owners in the use their phones to look up
health information.
 Latinos, African Americans, and those age 18-49 are more likely
than Caucasians and other older age groups to look up health
information on their phones.
 >100,000 mHealth apps marketed for mobile devices yet only
100 are FDA-approved.
 Privacy remains understudied in the context of mHealth in
treatment of SUD.
Results: Technology use patterns (N=85)
Results: mHealth use patterns
 35% used their mobile phone to access health information.
 85% would use a mobile app regularly to receive recovery
support.
13
30
13 13
8
9
0
5
10
15
20
25
30
35
NumberRespondents(N=85)
Frequency of engagement
How often would you like to use a smart phone app to
receive supportive, educational, or therapeutic
counseling to help your recovery?
Our sample was predominately African-American (47%), male (85%),
and completed high school (68%), unemployed or reliant on public
assistance (68%), and lacked permanent housing (52%). Average age
was 46.
Mobile phones owned (past 12 mo.) 4
Mobile phone numbers (past 12 mo.) 3
Smartphone owners 49 (57%)
Subscribers to unlimited text plans 63 (74.1%)
Prefer contact by text message 14 (16.5%)
Prefer contact by voice call 39 (45.9%)
No preference for contact type 31 (36.4%)

2. References & Acknowledgment
1. Healthcare Information Management Systems Society (2015). mHealth Definitions
(NIH Consensus Group). Retrieved from
http://www.himss.org/ResourceLibrary/GenResourceDetail.aspx?ItemNumber=20221
2. Fox, S. & Duggan, M. (2012) Mobile Health 2012. Pew Research Center’s Internet &
American Life Project. Retrieved from http://www.pewinternet.org/files/old-
media//Files/Reports/2012/PIP_MobileHealth2012_FINAL.pdf
3. Cortez, N. G., Cohen, I. G., & Kesselheim, A. S. (2014). FDA regulation of mobile health
technologies. New England Journal of Medicine, July 14, 2014, 372-379.DOI:
10.1056/NEJMhle1403384
Conclusions
 Generally high acceptance levels for mHealth-based interventions to
facilitate addiction treatment despite prevalence of privacy
concerns.
 User-tailored message content can inform app design to address
consumer privacy and comfort.
Acknowledgment: The SARET program is funded by a grant from the National Institute on
Drug Abuse (NIDA 5R25DA022461).
Discussion
 Technology and mHealth use patterns among participants were
consistent with nationwide user trends.
 Substance use terminology used in message content may trigger
discomfort among users and may be seen by peers, family members,
or the police.
 mHealth intervention design must be uniquely tailored to address
patients’ needs for trust, privacy, and user-friendliness.
 Limitations include potential for social desirability bias among
patients seeking addiction treatment; treatment-seeking patients
may also be more motivated to adopt mHealth.
Results: Privacy
Trigger Term Incidence (N=85)
HIV 31
Heroin 28
Hepatitis 26
Withdrawal 23
Alcoholic 20
Addiction 18
Drug 18
Methadone 17
Alcohol 17
Rehab 16
Cravings 16
Suboxone 14
Substance Use 12
Recovery 7
Treatment 6
Xanax 1
Results: mHealth use patterns (cont.)
mHealth preferences for receiving or sending information about (n=85)
Acceptability and privacy concerns for mHealth interventions among inpatient detoxification program patients
Grazioli F, Perna M, Thomas A, MD, Lee JD MD, MSc, Tofighi B MD, MSc
SARET Program, New York University
17
12
9
10
26
10
12
13
11
2
2
2
3
1
2
2
2
6
4
4
1
5
4
3
2
0% 20% 40% 60% 80% 100%
Medication reminders
Coping strategies to reduce
anxiety/depression
Smoking cessation
Tips to reduce alcohol/drug use
Appointment reminders
HIV prevention/treatment information
HCV prevention/treatment information
Sending HIV/HCV tx info to peers
Send addiction tx to info to peers
Text Smartphone app Website
 19% of respondents reported ever having had their mobile phone
accessed in a way that affected their privacy.
 47% of respondents were somewhat concerned or very concerned
about the privacy of their texts.
 SUD-related terms trigger user discomfort in text messages.
(See right)
22
18
1
11
32
0
5
10
15
20
25
30
35
Very Somewhat Unsure Not Very Not at All
Numberrespondents(N=85)
Level of Privacy Concern
How concerned are you with the privacy of your
texts?